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DilaPolatgil/
distilbert_finetuned_Turkish_New

Name: distilbert_finetuned_Turkish_New
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-uncased

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distilbert_finetuned_Turkish_New

This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0001
Overall Precision: 1.0
Overall Recall: 1.0
Overall F1: 1.0
Overall Accuracy: 1.0
Brand F1: 1.0
Brand Precision: 1.0
Brand Recall: 1.0
Chassis F1: 1.0
Chassis Precision: 1.0
Chassis Recall: 1.0
Date F1: 1.0
Date Precision: 1.0
Date Recall: 1.0
Ip F1: 1.0
Ip Precision: 1.0
Ip Recall: 1.0
Name F1: 1.0
Name Precision: 1.0
Name Recall: 1.0
Patientid F1: 1.0
Patientid Precision: 1.0
Patientid Recall: 1.0
Phonenumber F1: 1.0
Phonenumber Precision: 1.0
Phonenumber Recall: 1.0
Plate F1: 1.0
Plate Precision: 1.0
Plate Recall: 1.0
Policy F1: 1.0
Policy Precision: 1.0
Policy Recall: 1.0
Socialsecurity F1: 1.0
Socialsecurity Precision: 1.0
Socialsecurity Recall: 1.0
Studentid F1: 1.0
Studentid Precision: 1.0
Studentid Recall: 1.0
Taxnumber F1: 1.0
Taxnumber Precision: 1.0
Taxnumber Recall: 1.0
Tcno F1: 1.0
Tcno Precision: 1.0
Tcno Recall: 1.0
Vehicleregistration F1: 1.0
Vehicleregistration Precision: 1.0
Vehicleregistration Recall: 1.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.2
num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy	Brand F1	Brand Precision	Brand Recall	Chassis F1	Chassis Precision	Chassis Recall	Date F1	Date Precision	Date Recall	Ip F1	Ip Precision	Ip Recall	Name F1	Name Precision	Name Recall	Patientid F1	Patientid Precision	Patientid Recall	Phonenumber F1	Phonenumber Precision	Phonenumber Recall	Plate F1	Plate Precision	Plate Recall	Policy F1	Policy Precision	Policy Recall	Socialsecurity F1	Socialsecurity Precision	Socialsecurity Recall	Studentid F1	Studentid Precision	Studentid Recall	Taxnumber F1	Taxnumber Precision	Taxnumber Recall	Tcno F1	Tcno Precision	Tcno Recall	Vehicleregistration F1	Vehicleregistration Precision	Vehicleregistration Recall
0.5073	1.0	610	0.0012	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0021	2.0	1220	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0005	3.0	1830	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0003	4.0	2440	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	5.0	3050	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	6.0	3660	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	7.0	4270	0.0001	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

DilaPolatgil/distilbert_finetuned_Turkish_New

distilbert_finetuned_Turkish_New

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DilaPolatgil/
distilbert_finetuned_Turkish_New